There are several ways by which our approach can be extended. For estimating the correlations between channels, the dot-product can be replaced by kernel functions. The application of kernel functions in image style-transfer is an active research direction, and some of these methods should be applicable to our task [1]}. The property of Gram-Matrix being a symmetric positive semi-definite matrix can be explored in several ways, using e.g. statistical divergences (e.g. Kullback-Leibler, Jensen-Shannon, a.o.). Alternative training/finetuning schemes (e.g. prototypical, triplet, contrastive and self-supervised losses [2]}, [3]}, [4]}, [5]}) that do not require a classification head are probably better suited for such kind of experimentation. Attention methods are also worth-examining, e.g. for assigning data-driven weights in the time axis before estimating the correlation coefficients. Finally, the whole chain of transforms we apply on the tensor \({\mathbf {Y}}\)  is not a result of exhaustive experimentation, leaving room for further improvements.
